In the latter half of the twentieth century, there began a phenomenon known as the information revolution. While the information revolution is a historical development broader in scope than any one event or machine, no single device has come to represent the information revolution more than the digital electronic computer. The development of computer systems has surely been a revolution. Each year, computer systems grow faster, store more data, and provide more applications to their users.
A modern computer system typically comprises one or more central processing units (CPU) and supporting hardware necessary to store, retrieve and transfer information, such as communication buses and memory. It also includes hardware necessary to communicate with the outside world, such as input/output controllers or storage controllers, and devices attached thereto such as keyboards, monitors, tape drives, disk drives, communication lines coupled to a network, etc. The CPU or CPUs are the heart of the system. They execute the instructions which comprise a computer program and direct the operation of the other system components.
From the standpoint of the computer's hardware, most systems operate in fundamentally the same manner. Processors are capable of performing a limited set of very simple operations, such as arithmetic, logical comparisons, and movement of data from one location to another. But each operation is performed very quickly. Sophisticated software at multiple levels directs a computer to perform massive numbers of these simple operations, enabling the computer to perform complex tasks. What is perceived by the user as a new or improved capability of a computer system is made possible by performing essentially the same set of very simple operations, but doing it much faster, and thereby enabling the use of software having enhanced function. Therefore continuing improvements to computer systems require that these systems be made ever faster.
The overall speed of a computer system (also called the throughput) may be crudely measured as the number of operations performed per unit of time. Conceptually, the simplest of all possible improvements to system speed is to increase the clock speeds of the various components, and particularly the clock speed of the processor(s). E.g., if everything runs twice as fast but otherwise works in exactly the same manner, the system will perform a given task in half the time. Enormous improvements in clock speed have been made possible by reduction in component size and integrated circuitry, to the point where an entire processor, and in some cases multiple processors along with auxiliary structures such as cache memories, can be implemented on a single integrated circuit chip. Despite these improvements in speed, the demand for ever faster computer systems has continued, a demand which can not be met solely by further reduction in component size and consequent increases in clock speed. Attention has therefore been directed to other approaches for further improvements in throughput of the computer system.
Without changing the clock speed, it is possible to improve system throughput by using multiple processors. The modest cost of individual processors packaged on integrated circuit chips has made this approach practical. Although the use of multiple processors creates additional complexity by introducing numerous architectural issues involving data coherency, conflicts for scarce resources, and so forth, it does provide the extra processing power needed to increase system throughput.
Various types of multi-processor systems exist, but one such type of system is a massively parallel nodal system for computationally intensive applications. Such a system typically contains a large number of processing nodes, each node having its own processor or processors and local (nodal) memory, where the nodes are arranged in a regular matrix or lattice structure for inter-nodal communication. The inter-nodal communications lattice allows different sub-processes of an application executing in parallel on different nodes to exchange data with one another. Typically, such a system further contains a control mechanism for controlling the operation of the nodes, and an I/O mechanism for loading data into the nodes from one or more I/O devices and receiving output from the nodes to the I/O device(s). In general, each node acts as an independent computer system in that the addressable memory used by the processor is contained entirely within the processor's local node, and the processor has no capability to directly reference data addresses in other nodes. However, the control mechanism and I/O mechanism are shared by all the nodes.
A massively parallel nodal system such as described above is a general-purpose computer system in the sense that it is capable of executing general-purpose applications, but it is designed for optimum efficiency when executing parallel, computationally intensive applications, i.e., applications in which the proportion of computational processing and communication among parallel processes relative to I/O processing and I/O data transfer is relatively high. In such an application environment, most of the data entering and leaving a node is being communicated to other nodes as part of the application being processed in parallel. Therefore, it is important that the inter-nodal communications mechanism be designed to accommodate a large volume of data. Such an inter-nodal communications mechanism should support communication between any arbitrary pair of nodes (to avoid placing limitations on the types of applications which can be executed), but need not support communications between all possible node pairs with equal efficiency or latency. An inter-nodal data communications lattice provides a set of node-to-node communications links arranged in a regular pattern likely to be useful for processing large processing applications in parallel, without providing a direct connection between any two arbitrary nodes. Data can be sent via this lattice between any arbitrary pair of nodes either directly (where such a direct connection exists) or by passing through one or more intermediate nodes.
An exemplary massively parallel nodal system is the IBM Blue Gene™ system. The IBM Blue Gene system contains many processing nodes, each having multiple processors and a common local (nodal) memory. The processing nodes are arranged in a logical three-dimensional torus network having point-to-point data communication links between each node and its immediate neighbors in the network. Additionally, each node can be configured to operate either as a single node or multiple virtual nodes (one for each processor within the node), thus providing a fourth dimension of the logical network. A large processing application typically creates one ore more blocks of nodes, herein referred to as communicator sets, for performing specific sub-tasks during execution. The application may have an arbitrary number of such communicator sets, which may be created or dissolved at multiple points during application execution.
Where it is necessary to route data through one or more intermediate nodes of the inter-nodal communications lattice, there are generally multiple possible routes, and some methodology will be used to determine the routing. For ease of implementation and low management overhead, it is generally desirable to implement a simple, deterministic routing policy, such as a policy in which the locations of the sending node and receiving node automatically determine a routing, so that all data from a given sender to a given receiver follows the same route. Such a policy often produces good results, but there are situations in which it is less than optimal.
Frequently, network traffic is not randomly or evenly distributed, but is bursty in nature. In particularly, in some environments, a relatively small number of sending nodes are generating a disproportionately large volume of traffic to respective receiving nodes. Such uneven distribution of network traffic causes certain links to become heavily burdened while other links are underutilized.
Improper routing of messages and distribution of network traffic in a massively parallel system can significantly affect the overall performance of the system. Large applications executing in such systems often require substantial inter-nodal communication of data. Network bottlenecks increase latency times for data to be exchanged, and may cause sub-processes executing in individual nodes to wait for data from other nodes, further affecting performance.
A need exists for improved tools or methods for routing data in an inter-nodal communications network of a massively parallel system.